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import torch
import numpy as np
from . import utils
from utils import torch_device
import matplotlib.pyplot as plt
def get_unscaled_latents(batch_size, in_channels, height, width, generator, dtype):
"""
in_channels: often obtained with `unet.config.in_channels`
"""
# Obtain with torch.float32 and cast to float16 if needed
# Directly obtaining latents in float16 will lead to different latents
latents_base = torch.randn(
(batch_size, in_channels, height // 8, width // 8),
generator=generator, dtype=dtype
).to(torch_device, dtype=dtype)
return latents_base
def get_scaled_latents(batch_size, in_channels, height, width, generator, dtype, scheduler):
latents_base = get_unscaled_latents(batch_size, in_channels, height, width, generator, dtype)
latents_base = latents_base * scheduler.init_noise_sigma
return latents_base
def blend_latents(latents_bg, latents_fg, fg_mask, fg_blending_ratio=0.01):
"""
in_channels: often obtained with `unet.config.in_channels`
"""
assert not torch.allclose(latents_bg, latents_fg), "latents_bg should be independent with latents_fg"
dtype = latents_bg.dtype
latents = latents_bg * (1. - fg_mask) + (latents_bg * np.sqrt(1. - fg_blending_ratio) + latents_fg * np.sqrt(fg_blending_ratio)) * fg_mask
latents = latents.to(dtype=dtype)
return latents
@torch.no_grad()
def compose_latents(model_dict, latents_all_list, mask_tensor_list, num_inference_steps, overall_batch_size, height, width, latents_bg=None, bg_seed=None, compose_box_to_bg=True, use_fast_schedule=False, fast_after_steps=None):
unet, scheduler, dtype = model_dict.unet, model_dict.scheduler, model_dict.dtype
if latents_bg is None:
generator = torch.manual_seed(bg_seed) # Seed generator to create the inital latent noise
latents_bg = get_scaled_latents(overall_batch_size, unet.config.in_channels, height, width, generator, dtype, scheduler)
# Other than t=T (idx=0), we only have masked latents. This is to prevent accidentally loading from non-masked part. Use same mask as the one used to compose the latents.
if use_fast_schedule:
# If we use fast schedule, we only compose the frozen steps because the later steps do not match.
composed_latents = torch.zeros((fast_after_steps + 1, *latents_bg.shape), dtype=dtype)
else:
# Otherwise we compose all steps so that we don't need to compose again if we change the frozen steps.
composed_latents = torch.zeros((num_inference_steps + 1, *latents_bg.shape), dtype=dtype)
composed_latents[0] = latents_bg
foreground_indices = torch.zeros(latents_bg.shape[-2:], dtype=torch.long)
mask_size = np.array([mask_tensor.sum().item() for mask_tensor in mask_tensor_list])
# Compose the largest mask first
mask_order = np.argsort(-mask_size)
if compose_box_to_bg:
# This has two functionalities:
# 1. copies the right initial latents from the right place (for centered so generation), 2. copies the right initial latents (since we have foreground blending) for centered/original so generation.
for mask_idx in mask_order:
latents_all, mask_tensor = latents_all_list[mask_idx], mask_tensor_list[mask_idx]
# Note: need to be careful to not copy from zeros due to shifting.
mask_tensor = utils.binary_mask_to_box_mask(mask_tensor, to_device=False)
mask_tensor_expanded = mask_tensor[None, None, None, ...].to(dtype)
composed_latents[0] = composed_latents[0] * (1. - mask_tensor_expanded) + latents_all[0] * mask_tensor_expanded
# This is still needed with `compose_box_to_bg` to ensure the foreground latent is still visible and to compute foreground indices.
for mask_idx in mask_order:
latents_all, mask_tensor = latents_all_list[mask_idx], mask_tensor_list[mask_idx]
foreground_indices = foreground_indices * (~mask_tensor) + (mask_idx + 1) * mask_tensor
mask_tensor_expanded = mask_tensor[None, None, None, ...].to(dtype)
if use_fast_schedule:
composed_latents = composed_latents * (1. - mask_tensor_expanded) + latents_all[:fast_after_steps + 1] * mask_tensor_expanded
else:
composed_latents = composed_latents * (1. - mask_tensor_expanded) + latents_all * mask_tensor_expanded
composed_latents, foreground_indices = composed_latents.to(torch_device), foreground_indices.to(torch_device)
return composed_latents, foreground_indices
def align_with_bboxes(latents_all_list, mask_tensor_list, bboxes, horizontal_shift_only=False):
"""
Each offset in `offset_list` is `(x_offset, y_offset)` (normalized).
"""
new_latents_all_list, new_mask_tensor_list, offset_list = [], [], []
for latents_all, mask_tensor, bbox in zip(latents_all_list, mask_tensor_list, bboxes):
x_src_center, y_src_center = utils.binary_mask_to_center(mask_tensor, normalize=True)
x_min_dest, y_min_dest, x_max_dest, y_max_dest = bbox
x_dest_center, y_dest_center = (x_min_dest + x_max_dest) / 2, (y_min_dest + y_max_dest) / 2
# print("src (x,y):", x_src_center, y_src_center, "dest (x,y):", x_dest_center, y_dest_center)
x_offset, y_offset = x_dest_center - x_src_center, y_dest_center - y_src_center
if horizontal_shift_only:
y_offset = 0.
offset = x_offset, y_offset
latents_all = utils.shift_tensor(latents_all, x_offset, y_offset, offset_normalized=True)
mask_tensor = utils.shift_tensor(mask_tensor, x_offset, y_offset, offset_normalized=True)
new_latents_all_list.append(latents_all)
new_mask_tensor_list.append(mask_tensor)
offset_list.append(offset)
return new_latents_all_list, new_mask_tensor_list, offset_list
@torch.no_grad()
def compose_latents_with_alignment(
model_dict, latents_all_list, mask_tensor_list, num_inference_steps, overall_batch_size, height, width,
align_with_overall_bboxes=True, overall_bboxes=None, horizontal_shift_only=False, **kwargs
):
if align_with_overall_bboxes and len(latents_all_list):
expanded_overall_bboxes = utils.expand_overall_bboxes(overall_bboxes)
latents_all_list, mask_tensor_list, offset_list = align_with_bboxes(latents_all_list, mask_tensor_list, bboxes=expanded_overall_bboxes, horizontal_shift_only=horizontal_shift_only)
else:
offset_list = [(0., 0.) for _ in range(len(latents_all_list))]
composed_latents, foreground_indices = compose_latents(model_dict, latents_all_list, mask_tensor_list, num_inference_steps, overall_batch_size, height, width, **kwargs)
return composed_latents, foreground_indices, offset_list
def get_input_latents_list(model_dict, bg_seed, fg_seed_start, fg_blending_ratio, height, width, so_prompt_phrase_box_list=None, so_boxes=None, verbose=False):
"""
Note: the returned input latents are scaled by `scheduler.init_noise_sigma`
"""
unet, scheduler, dtype = model_dict.unet, model_dict.scheduler, model_dict.dtype
generator_bg = torch.manual_seed(bg_seed) # Seed generator to create the inital latent noise
latents_bg = get_unscaled_latents(batch_size=1, in_channels=unet.config.in_channels, height=height, width=width, generator=generator_bg, dtype=dtype)
input_latents_list = []
if so_boxes is None:
# For compatibility
so_boxes = [item[-1] for item in so_prompt_phrase_box_list]
# change this changes the foreground initial noise
for idx, obj_box in enumerate(so_boxes):
H, W = height // 8, width // 8
fg_mask = utils.proportion_to_mask(obj_box, H, W)
if verbose:
plt.imshow(fg_mask.cpu().numpy())
plt.show()
fg_seed = fg_seed_start + idx
if fg_seed == bg_seed:
# We should have different seeds for foreground and background
fg_seed += 12345
generator_fg = torch.manual_seed(fg_seed)
latents_fg = get_unscaled_latents(batch_size=1, in_channels=unet.config.in_channels, height=height, width=width, generator=generator_fg, dtype=dtype)
input_latents = blend_latents(latents_bg, latents_fg, fg_mask, fg_blending_ratio=fg_blending_ratio)
input_latents = input_latents * scheduler.init_noise_sigma
input_latents_list.append(input_latents)
latents_bg = latents_bg * scheduler.init_noise_sigma
return input_latents_list, latents_bg
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